A new paradigm: the wealth effect of the stock market on consumption, in a context of interacting bio-systems

Journal of Socio-Economics 31 (2002) 75–100

A new paradigm: the wealth effect of the stock market on consumption, in a context of interacting bio-systems Samuel B. Bulmash∗ College of Business Administration, University of South Florida, Tampa, FL 33620, USA

Abstract This study examines the existence of a stock market wealth effect on aggregate private consumption in the economy. A theory of investor/consumer behavior is suggested, in a context of a symbiotic relationship between two quasi-bio-systems. The model offers predictions about likely outcomes in capital market interaction with the underlying economy in general and consumption in particular. These predictions are validated empirically. Specifically, the paper finds that investors/consumers do not respond immediately to a stock market rise (fall). Rather, they wait at first and thereafter gradually accelerate their ‘wealth spending’ on consumption only after they are convinced that the gain is permanent (a variation of the ‘income smoothing’ that was suggested many years ago by Friedman (1957)). The paper suggests that the capital markets and the economy interact like two bio-systems symbiotically responding to each other. This study presents evidence that the consumption wealth spending peaks at approximately 2.5% of the stock market wealth CUMULATIVE gain in the previous 12–24-month period, with some effects lingering on up to 36 months. For example, it shows that over 40% of the growth in consumer spending in 1999 was attributable to gains in the stock market in previous years, contributing to a strong GDP in that year. © 2002 Elsevier Science Inc. All rights reserved. Keywords: Wealth effect; Stock market; Bio-systems

1. Introduction This paper presents evidence about “wealth effect” interaction between the stock market and the economy. It offers an interpretation of this relationship in the context of a symbiotic interaction between two quasi-bio-systems: the stock market and the underlying economy. The wealth effect that is measured and reported can be interpreted both ways—one being the ∗

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“symbiotic” way and the other—using the conventional financial economics context. One does not preclude the other with respect to the wealth effect. The relevance of the wealth effect is easily obvious: During the decade of the 1990s, the value of the Wilshire 5000 index (which actually has over 7,000 stocks and roughly approximates the value of all outstanding shares on the various stock markets in the U.S.A.), has increased from $3,419 trillion dollars to $13,812 trillion, an increase of 4.039-fold. At the same time, the Gross Domestic Product (GDP) increased from $5,537 trillion to an estimated $9.5 trillion, an increase of over 1.6-fold. The Private Consumption Expenditures (PCEs) increased from $3,705 trillion to an estimated $6.3 billion, a 1.7-fold increase. The “economic boom” of the 1990s is considered one of the longest in duration in recent U.S.A. history. An important question is therefore, obvious—does the change in stock market values have an impact, or a ‘wealth effect’ on the underlying economy? And if it does—what is the process that produces this impact? This study tries to answer these questions while offering some old and some new explanations. This paper makes several contributions, some of which are quite controversial at this time. (1) It will be shown in this paper that while the ‘wealth effect’ is quite small in the short run, it becomes gradually more significant if the gains (losses) continue for a time long enough to shift investors’ beliefs. The shift is gradual and enables stock investors to view their stock market gains (loss) as a permanent increase addition (loss) to their wealth. Its cumulative importance rises when longer time intervals are considered, up to certain level that will be explored in this paper. Based on the regression coefficients for the various time intervals, it is shown that the stock market gains in 1997 and 1998 had a combined contribution well over 40% of the increase in consumer spending on consumption in 1999. Likewise, the stock market gains in 1998 and 1999 are estimated to contribute a similar share towards consumption in the year 2000. The reader can draw conclusions as to the slow down in the economy in 2001 due to cooling off in the stock market in the year 2000. The model is significant also in implying that due to the wealth effect, monetary policy in the current era has to affect the stock market if it is to affect the economy, in addition to the traditional channels. The implication is that otherwise the wealth effect can partly offset the impact that the Federal Reserves’ steps of raising (lowering) interest would have had on the economy. Very little, if any, of the 1999 consumption was due directly to stock markets gains in 1999, although the existence of such concurrent stock gains may have had a psychological effect in allowing consumers to feel secure about consuming earlier stock market wealth gains. (2) The paper suggests that investors’ behavior, make the stock market as a whole resemble a “semi-bio-system” that interacts in a symbiotic relationship with the underlying economy. While this may seem a controversial suggestion, it is not too far off Coase’s (2000) suggestion that “the economic system is very much like a human body with all its different parts interrelating, one with another.” Nor is it too far off Keynes (1936) reference to “animal spirits” that drive investors and consumers in a self-fulfilling behavior.

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This paper takes earlier contributions to behavioral finance and macro-finance one step further, by aligning them with other contributions from neurology and investors’ psychology, and suggests that the interaction between the stock market and the underlying economy can be viewed as a “symbiotic, or adaptive, relation.” The interaction involves continuous feedback between the stock market and the economy systems and their adaptation to each other. In the short run, investors as a group, exhibit ‘herd behavior’ and respond to economic news with a mixture of rational and emotional reactions in their stock investments. But, being aware of this exaggeration and uncertainty, they are afraid of losing their recent stock gains, so at first they refrain from spending them on consumption. In the long run, however, the relation becomes more rational and more symbiotic-interdependent. If the conditions are supportive, the capital market grows and enables more economic growth to be created, as will be shown later. If the conditions are non-supportive, some economic value is destroyed and some capital is destroyed as well, while some capital emigrates elsewhere to other more supportive environments. Moreover, we see this migration of capital not only across geographic boundaries, but also between stock markets in the same country. In the 1970s and 1980s, investors shifted their capital between stocks and bonds, whenever one type of investment was perceived to be more rewarding for an equivalent risk. However, by the end of 1999 and the first few months of the year 2000, investors responded to the Federal Reserve (hence FED) threats of raising interest rate by mostly shifting capital out of the bond market and into the NASDAQ. They focused on technology stocks that will, in their estimate, perform well in a strong economy. Investors that got used to a prolonged period of high returns in stocks were not content to accept single digit returns in bonds or in money market funds, and convinced themselves that NASDAQ stocks represent a “new economy” that is immune to the pain of rising interest rate. This behavioral response considerably frustrated the FEDs efforts to cool off the overall stock market and the economy. The ‘bio-system’ theory presented in this paper would have predicted that capital investors would find a new way to adjust to the new challenge, similarly to the way that a bio-system tries to adjust to its changing environment. As is suggested through this paper, the source of this interaction is investors’ behavior. When they are optimistic – they overestimate the future expected stock investment returns and thus they provide too much capital to corporations, who in turn over-invest, thus setting the stage for lower returns on capital later on. While the over-investing goes on, the increased hiring by corporations and increased spending by consumers (who feel wealthier) helps sustain and feed into the overall sense of a stronger economy that in turn encourages further capital flow from investors to the stock market. Little attention is paid during that period to the potential onset of creating excess capacity and lower future returns, as the “euphoric crowd behavior” affects consumers, the media, investment Analysts and corporate executives (see for example the DeBondt and Forbes (1998) paper on herding behavior among analysts). The concurrent link from stock gains to GDP growth (through private consumption) should therefore be initially small (as found by Higgins (1988)). But, in a deviation from Higgin’s (1988) study, I suggest that as the capital gains become more persistent, investors begin to view them as permanent additions to their income and wealth and adjust their investments accordingly, thereby providing a further feedback from the stock market to the economy.

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This could explain an asymmetry that was observed by earlier studies. Stock market values were found (here and in earlier studies) to react faster to expected real gains to the economy than the other way around. This is, according to a classic Gordon model, because stock values represent investors’ perceptions of discounted streams of future cash flows from the underlying corporations. However, the GDP should react slower and more gradually to capital market gains (losses), since according to this paper’s findings, the increase in consumption occurs only after a while. Hence, it is not surprising that by 1999, the ratio of GDP to the Wilshire 5000 stood, as reported earlier, at 1.4, nearly double its average in the 1970s and 1980s. Yet, taking a longer perspective-during the entire 1970–1999 period, the regression coefficient of GDP on Wilshire 5000 (and vice versa) was about 1! The empirical evidence presented later in the paper supports the model and its predictions. The results and their interpretations have considerable implications for investors, students of the markets and for regulators, as will be discussed later on. 1.1. The symbiotic interaction concept This paper suggests that this symbiotic interaction and macro-systems co-dependency, provide an analogy of the capital markets (in the U.S.A. and probably in other industrial countries as well, although they are outside the scope of this paper) to a bio-system that behaves in a deterministic manner. More specifically, I suggest that capital can be viewed as a ‘Quasi-Bio-System’ (hence QB-system), that behaves as if it is ‘quasi-alive’ in its own different way. As such, it needs to continuously to interact with the host economy in a symbiotic relationship that feeds and nurtures back and forth. An indirect, additional contribution of the paper is related to the debate about mean reversion: DeBondt and Thaler (1985, 1989) argued that mean reversion takes place when the stock market valuation relative to the economy deviates from its ‘normal’. The empirical findings (presented later) show that the relationship between the two (stock market and the underlying economy) tended in the long run (in the 1970 through 1999 period) to converge around some ‘mean’ ratio of about 1.0, with the ‘mean’ itself adapting gradually over time rather than staying static (more precisely, it converged around 0.7 in the 1970s and 1980s, and above 1.2 in the 1990s). Earlier mean reversion studies, including DeBondt and Thaler (1985, 1989) do not provide sufficient explanation for such a scale shift. However, the scale shift is consistent with the ‘bio-system’ adaptive behavior offered in this paper. Moreover, this paper suggests that the ‘mean reversion’ is complicated and partially offset by two other concurrent processes—a modest ‘wealth effect’ that is a product of the symbiotic relationship between a economy and its stock market, and a strong ‘animal spirits’ (or ‘herd effects’), that can carry deviations for an extended period of time before converging back. Hence, the definition of the stock market as a ‘quasi-bio-system’ with a symbiotic relationship with the host economy. This symbiotic interaction produces the ‘wealth effect’. This ‘wealth effect’ enables the stock market to contribute to the growth of the economy, but as expectations for feedback reinforce themselves and feed back into stock valuations, the stock market may increase over time above its previous ‘normal’ size relative to the underlying economy. The information transmission in the modern economy is very fast and often accompanied by instant reporting and interpretations by ‘experts’ and the media. This is analogous

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to the neurotransmitters transmitting signals in the human body. However, these transmitters (called ‘legands’ in neurology) need receptors that fit the ‘shape’ of the ‘legands’ for a successful transmission of information to take place. In our context, the media can provide instant information and analysts and investors react to it, but the new information may not fit their existing perceptions, so the reaction may or may not be adequate for the contents that has been transmitted. It may take time and gradually adjusting perceptions for a fit to occur between the information transmittal and the receptors of the information. Likewise, time may pass from the point of information reception until a chemical or kinetic change in behavior occurs in a bio-system. This analogy is consistent with reported reluctance of individual stock analysts and investors to deviate too far from the ‘herd’ due to ‘sticking with old beliefs’. According to recent studies, this psychological investor behavior leads to gradual rather than full immediate response to new earnings reports (see DeBondt and Forbes, 1998; Jagadeesh and Titman, 1993; Kahneman and Riepe, 1998 for more on this point). It should be emphasized here that although the paper offers an interpretation of a ‘symbiotic’ interaction between bio-systems that can explain some of these observations, alternative explanations are not ruled out. The possible co-existence of more traditional explanations along side the ‘bio-system’ explanation, does not preclude the later. The rest of the paper proceeds as follows: Section 2 provides more literature perspective relevant to this paper. Section 3 presents the ‘wealth effect’ model, both in the ‘symbiotic bio-system’ context and in a conventional financial economics context. Section 4 presents the empirical findings and Section 5 summarizes the paper and its conclusions.

2. A literature perspective of the issues raised by this paper A very large body of literature examines the relation between the stock market and the economy and how investors react to economic news. Some studies take a mechanical approach, viewing stock valuations as a rational discounted value of future cash flows. Others look more into the emotional responses of investors, where these behavioral aspects determine demand and supply and subsequent stock prices. This paper tries to link the two approaches, although it is leaning more towards the behavioral interpretation, with some caveats. The existence of a ‘wealth effect’ has been frequently suggested in the business media, mostly in linking consumer spending with the surging stock market of the 1990s. However, in academic circles, it got less attention. Some studies found very little evidence of a ‘wealth effect’ or any significant ability of the stock market to predict future economic activity (see, e.g., the Kidwell et al., 2000 textbook and Higgins, 1988). Although not espousing outright ‘bio-system’ interpretations, Middleton (1996) and Farmer and Guo (1994), Weil (1989), Howitt and McAfee (1992), Kupic and Sharp (1991) and Keynes (1936) have suggested that investors display ‘animal spirits’ in their responses to economic news. This behavior is capable of creating ‘self-fulfilling’ beliefs that affect economic behavior. My paper goes on from these earlier contributions one step further with the ‘bio-system’ theory of capital and the stock market.

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As stated earlier, it goes on to suggests that Capital can be viewed as a ‘Quasi-Bio-System’ that behaves as if it is ‘quasi-alive’ in its own different way. As such, it needs to continuously interact with the host economy in a symbiotic relationship that feeds back and nurtures both, although the economy’s impact on the stock market is more immediate than the other way around. An important aspect of the symbiotic interaction is the transmission process from the capital market to the economy and back. Any blockage in one or more of the symbiotic transmission channels (massive failure of financial intermediaries, political changes that interrupt the transmission, as just a couple of examples), can interrupt the interaction and cause “sickness”, either in the economy or in the capital market or in both. (See Pert (1997) for more on the mind–body interaction and information transmission in the body) These symbiotic relationship and the ‘quasi-bio-system’ analogy propositions, as controversial as they may seem, can also explain many earlier puzzles that confounded researchers. For example, Fama (1990) and Jensen et al., 1996 find a strong link between the stock market and the economy. Yet, Jagannathan and Wang (1996) disagree and claim that stocks form only a small part of aggregate wealth (which would be therefore, inconsistent with the existence of a ‘wealth effect’). Remolona (1991) finds that global stock markets tend to overreact to each other and to their underlying real economies. At first it may seem to be inconsistent with the Fama (1990) and Jensen et al. (1996) implications. However, it is less contradictory, if we recall my interpretation—that the market interacts in a symbiotic way with the economy and that extended mutual growth can create expectations for further wealth effects that feed back into the stock market. This makes the stock market appear to move ahead of the economy. This interpretation implies that investors psychology (fears and euphoria) will cause the stock to be undervalued relative to the economy when the economy is weak (increasing risk aversion) and to be overvalued relative to the economy when the economy has been strong for a longer then usual time (diminishing risk aversion). However, these deviations violate the long run symbiotic relations and the stock market and the economy will have to eventually correct and adapt towards each other in the long run. A case in point, the ratio of the stock market’s total value (as proxied by the Wilshire 5000 index) to GDP was declining through most of the 1970s and was mostly flat (or converging around a constant) in the 1980s, but was exponentially increasing through most of the 1990s, creating a polynomial relationship of the second degree (‘U’ shaped parabola) over the period 1970–1999. Attempts to explain away this change as a mere ‘bubble’ are doing injustice to a process that the traditional approaches simply are not explaining adequately. Studies in behavioral finance (see, e.g., Tversky and Kahneman, 1986; DeBondt and Thaler, 1985, 1989; Kahneman and Riepe, 1998) have suggested that investors are not always rational economic entities, and that they react to fear and greed and display risk aversion in ways that are not necessarily always compatible with a rational economic behavior. Kahneman and Riepe (1998) point out that the human mind is a pattern-seeking device, and it is strongly biased to adopt the hypothesis that a causal factor is at work behind any notable sequence of events. They also suggest that investors and “financial experts” often are biased towards overconfidence in their abilities and over optimistic about the outcomes. They also argue that investors are reluctant to realize their loses (they call it “the disposition effect”). These views are shared also by Daniel et al. (1998), who point to investors’ overconfidence and

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biased self-attributes as leading to short-term autocorrelations (momentum) and long-term stock price reversals. All these factors lead them, and as well as Kahneman and Riepe (1998) to conclude that investors’ psychology plays a very important role in the stock market. DeBondt and Forbes (1998) present evidence of ‘herding in analysts forecast’ that causes them to mimic each other and to err systematically. This is consistent with evidence that most mutual fund managers fail over time to outperform the unmanaged SP500 index. Earlier, DeBondt and Thaler (1989) argued that the resulting investors’ errors and overreactions cause them later to reverse course and produce the mean reversion in stock returns. However, as mentioned in Section 1, Jagannathan and Wang (1996) find no evidence to support the mean reversion idea that DeBondt and Thaler (1989) proposed. My story of “adaptive quasi-bio-system” behavior by the stock market in its symbiotic relation with the underlying economy allows reconciliation of these various studies albeit with a different interpretation than suggested by those authors. The absence of ‘mean reversion’ in the findings of Jagannathan and Wang (1996) may be, in my story, due to the offsetting effects of the ‘mean reversion’ and of the ‘wealth effects’. Another reason may also be, in my interpretation, that psychological factors have mostly a short and intermediate term effect on the stock market, while underlying economic principles reassert themselves in the economy and the capital markets in the long run. The “herding behavior” that Cote and Goodstein (2001) and DeBondt and Forbes (1998) articulate may lead the herd to judgment errors see for e.g. McQueen et al. (1996) in the short run, but often, eventually, the herd realizes its mistakes and returns to sources of nutrition and shelter. This leads to another important aspect in this current paper, the existence of predictable linkage of between economic conditions and the stock market. A linkage is suggested in numerous studies, such as Haugen and Baker (1996), Fama (1990, 1993), Fama and French (1996), Bulmash and Trivoli (1991) and many others. Those studies imply that macro-economic fundamentals have predictive powers regarding the stock market, although they do not agree about the predictive powers of the stock market with respect to the economy. This paper provides more evidence supporting the later. To summarize this section, this study draws upon several schools of thought: the investors’ behavioral aspect and the linkage of stock markets to the underlying economic principles aspect. Thereafter, it interprets them in a framework that draws analogies from the biological sciences, regarding symbiotic relations, and integrates some of the elements into a new paradigm of the stock market. According to this new paradigm, the stock market can be viewed as a composite quasi-bio-system that behaves like a quasi-live organism and feeds upon and feeds back into the economy with which it interacts. Just as in nature a symbiotic interaction of two bio-systems can enable both to grow, I suggest that a proper interaction between the stock market and the economy can create wealth in both systems in a ‘wealth effect’ process that will be formally described in Section 3. The multi-phase model that is presented next is self-contained in the traditional economic structure and is not dependent on the “bio-system” paradigm, but is totally consistent with it as well, thus, lending it indirect support.

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3. A theory of the stock market as a quasi-bio-system in a continuous process of convergence, reaction and adaptation to the host economy The capital market is defined here as “Quasi-Bio-System” because it is not a real biological organism, but it displays many similarities to a biological system in its composition, reactions and behavior, and in the process exhibits determinism, intelligence, herd instincts, fear and exuberance, in ways that distinguish it from earlier studies. In this story, the financial markets display Darwinism in the relentless pursuit of capital of promising enterprises and capital’s exodus from entities that are considered ill adapted to the changing world. The capital markets reward those who seem to be the fittest and punish those who seem to be outdated. These patterns are more suggestive of a “bio-system” than of a “mechanical system.” In nature, a living organism needs a certain amount of space/territory or ‘body mass’ of the host in order to thrive. I suggest that to some extant one can draw parallels to my findings (presented in Section 4) that in the aggregate stock market in the U.S.A. tends to have a symbiotic relationship of adaptation and convergence to the economy in which it operates. I define the interaction process as “Symbiotic” and suggest that the capital market resembles a bio-system which is a complex entity consisting of millions of “cells” (investors), multiple organs (institutions providing funds and economic entities seeking funds), seeks self-preservation and growth and will evade dangers by migrating elsewhere to less threatening pastures. Capital (through its suppliers), always seek ‘nutritious’ and safe host-territory/economy that is supportive and enables it to grow. This symbiotic process is benefiting if the host environment also grows and prospers. As a composite of millions of intelligent but also emotional sub-units (individual investors and institutions) the capital market displays characteristics of a ‘quasi-biological’ system. It will likewise display intelligence and emotional behavior that is more typical of a living intelligence than of a robotic machination, hence the reference to it in this paper as a ‘quasi-bio-system’. Moreover, it will interact with its many sub-units in a way that is somewhat similar to the way the human body interacts with its organs, cells and nervous system. It sustains and protects them and performs well if they perform well but occasionally succumbs to illness and life cycle changes, either due to internal cellular changes or due to external environmental changes or due to blockage of “transmission channels” (for reasons such as hysteric or biased information delivery or no delivery at all, failure of financial intermediation process, etc.). This may interrupt supply from the ‘suppliers of capital’ to the ‘users of capital’. Again, one can find an analogy to problems in a living organism if there are neurological blockages that interrupt or bias signal transmissions from the nerve system to the organs in the body and vice versa. A human individual interacts with the society around it and the territory in which it and its society co-exist. In the aggregate, the capital is accumulated and moves in ways that make an analogy to bio-organism or a bio-system a compelling one. When such an accommodating ‘living space’ is available—investors are optimistic and provide capital in increasing amounts and this in turn further enables companies to grow and prosper and attract more capital which migrates from other areas to the one which is more rewarding. Similarly, when the economic basis (hosting and sustaining the capital market) significantly shrinks or is unstable, investors get scared away, capital formation shrinks, companies are deprived of essential capital and

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wither away and the loss in valuations and wealth scares even more capital away, and capital will migrate away. At the aggregate, the behavior of the stock market will be a reflection of its constituents-investors, reacting to optimism, pessimism, fear and greed, and in aggregate, the capital market (equity and to some extant fixed income as well) will behave as a quasi-bio-system adapting and reacting to its host (economy). In the wide range between the very optimistic and very pessimistic extremes, capital and economic activity interact in a fairly systematic way, converging and supporting each other and maintaining fairly steady ratios of size magnitudes relative to each other. Deviations may occur from time to time. When the magnitude of the quasi-bio (hence QB) system/capital exceeds that of the host economy, a bubble in asset prices may occur. Such a bubble is not sustainable in the long run, but can temporarily exist as long as the QB-system/capital determines that there is no better host available at that time to migrate to. When a better host presents itself, the QB-system/capital will migrate to it and the bubble will burst. The knowledge that the host and the QB-system/capital (and/or policy makers as well), have about the possibility for such migration and their respective dependence and interaction, can keep the host and the quasi-biosystem (QB-system) in a symbiotic relatively adaptive relation most of the time: Adjustments that may take place when a quasi-intelligent system (as the capital market is assumed to be in the context of this paper) reacts to its supporting environment. Put another way, the capital market’s millions of constituents investors, react to the economic information with an observable aggregate outcome relative to that economic information. In the context of this paper, this aggregate behavior is what defines the quasi-bio-system perspective of the capital market. Because of the symbiotic relations, a desertion of the QB-system (capital) robs the host of a vital interaction and mutual nourishment that the co-existence with the capital provided, and it will be worse off without it. In an another analogy, If the organism/capital grows too big relative to the supporting host/economy (due to excessive optimism by investors from inside or outside the economy/territory), two things may happen: (1) Either the new capital creates new opportunities for economic growth (symbiotic mutual growth) which enables both the host and the organism/capital to prosper. (2) If the host/economy is not growing for whatever reasons (inhospitable monetary or fiscal policy, wars, social conflict, technological stagnation etc.), the organism/capital will observe that the interaction with the host/economy is becoming less rewarding and will migrate elsewhere. Although such a migration can be very quick, it is possible for the QB-system-organism/capital to linger longer at a particular host/economy/territory, if as an intelligent entity it determines that there is no better host available at this time, or if it is slow to realize that a fundamental change has occurred. In that case, we may observe a disproportional magnitude of the organism relative to the host (BUBBLE in asset prices), relative to the more normal situation where the relative sizes of the capital market and the supporting host are relatively stable. Although such a bubble would eventually resolve itself, it is not necessary for it to be resolved immediately. It is quite possible for the capital to choose to stay in a less then perfect symbiotic relationship with the host for quite some time, as long as it finds no better alternative host. Moreover, as sometimes happens in symbiotic relationships, things sometimes get better after a while.

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When that happens, the organism/capital may determine that it is not worth to migrate if an improvement in the host situation may warrant a return to the host in a short while, as long as better host is not available. The paper points to linkages between the stock market value and the underlying economic streams, that by themselves tend to be predictable in the intermediate and long run. Therefore, the characteristic of serial correlation and predictability of stock prices and of economic trends that recent studies have reported (see Section 2)—are consistent with the predictions of this study, even though this paper differs from previous ones in its story. Indeed, in this story, the stock market is inter-linked both with past and future state of the economy, in continuous symbiotic relationship that creates and destroys value both either in the capital market and to a somewhat smaller extent—also in the underlying economy itself. The trend in their respective absolute levels is remarkably similar (as shown later in Section 4, Tables 1 and 2). If the particular economy is growing more then do other economies outside it, capital may migrate from those economies outside into this economy, and in the process the relation between the stock market value and the host economy may exceed its previous relative magnitude and an asset bubble may evolve. As long as the perceived relative reward to the capital is attractive enough relative to perceived opportunities elsewhere, the capital will chose to stay with the host economy as being preferable to migrating elsewhere. In the long run (the length of which may vary, depending on outside alternatives), however, the symbiotic relation will tend to bring the values of the stock market and the underlying host into alignment. An increase in stock market value can cause the following: (1) Companies can raise more capital to increase plants and inventories and will hire more people and increase output. Result: GDP will rise. (2) With increased financial wealth (due to rising stock values), consumers increase spending, thus pushing GDP up. In this case, “real value” was created following an increase in “financial value.” (3) Either factors (1) or (2) above will bring value creation that is passed on from the stock market to the real market. The stock market leads the real market, due to investors’ expectation for same, conditioned on them being self-fulfilling (“animal spirits” as defined by Keynes (1936)). (4) The initial optimism can be due to various reasons, such as more favorable monetary or fiscal policy by the government (that investors expect will positively affect output and profits), inside information about new promising business opportunities, “herd mentality” or similar reasons (the precise initial causes for optimism can be many). (5) If the value creation in the real market fails to catch up with the value creation in the financial market, it causes an increase in the ratio of stocks to investors “overall wealth, (defined later as W), relative to its historic level. The higher ratio implies that investors now have a greater portion of their wealth in stocks than otherwise they used to. Their willingness to do so is predicated on their expectations for further increases in stock values and/or lower perceived risk of owning stocks (hence, lower risk premium). This can go on for a while if additional real value” can be created in the real economy (further increase in GDP).

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(6) Technological innovations (such as greater use of computers or other innovations), or government policies, can provide an extended rise in GDP increases. However, if eventually the marginal output of capital begins to slow down (for example, the innovation has run its current course of implementation and expansion), investors will no longer tolerate k being above its “normal” level. They will consider further increases in GDP unlikely in the foreseeable near term, hence they will perceive stocks to be overvalued relative to economic fundamentals. At this point, risk premiums will rise, cost of financing will rise (companies will find it harder to finance new plant expansion), and the financial values will have to converge to the real perceived economic value reflected by the expected GDP. The excessive financial wealth will be destroyed as the “financial and real” wealth values converge towards each other. If too much financial wealth was destroyed, this can create a recession because companies cannot find new capital to finance new plants and also because consumers feel poor and spend less. (Some expectations along these lines were also raised in the business media. See for example Ip (2000)). This retrenchment process can lead to a severe recession and feed on itself, causing real value destruction that, in turn, can lead to downward revisions in investors SMV, creating financial value destruction that feeds into further real value destruction. This downward spiral is extreme and self-feeding, until the government intervenes and stimulates the economy by monetary or fiscal stimuli. These, in turn, can create positive shocks both to “economic value” (increased buying power and spending) and “financial value.” Does every downturn have to lead to a severe recession? Not necessarily. My model suggests that normally stock market value (hence SMV) and “the real economy value” (GDP) converge towards each other. Thus, if SMV was excessive relative to “real value,” the former would adjust downwards sufficiently to bring those two values into alignment. Only “excess financial value” would be destroyed, and once the two values converged, no further steps need to be taken by investors or consumers. The actual ratio will hover around the “historic level”, and volatility and returns would fluctuate randomly until new shocks arrive. Such shocks can be real (increased GDP, technological innovations, increased productivity, etc.) or financial (inflation, change in interest rates, change in money supply, increased (decreased) investor optimism and perceived risk premium, etc.). The “real shocks” would lead to GDP increasing relative to SMV and stocks would subsequently have to rise (due to increased buying power and revised expectations about future corporate earnings). DeBondt and Thaler (1985) suggested that investor overweight current information in creating expectations about the future. This argument fits partly into my model, with recent GDP increases affecting current investors’ expectations about future GDP, thus, leading them to buy stocks and creating financial value that feeds further real value, until an excess occurs as described earlier. Note that in my model, SMVs are both leading real GDP as well as following it. This is different from earlier models such as Gordon’s (1963) (where P = D1/R − G, with P stock price, D1 next period dividend, R required return, G expected future growth in dividends and earnings), that postulate that stock’s value look ahead to the future and not to the past. In contrast, Fama and French (1996) and Haugen and Baker (1996) argue that future stocks’ returns are predictable by current and past economic information.

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In my model of continuous economic and financial value convergence, the adjustment process is continuous, so SMV both leads GDP value as well as is lead by it. It allows for a build up of divergences from the normal values but predicts that eventually the divergence will be temporary and convergence will be restored. These arguments can be formalized in the following model:

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3.1. A multi-phase wealth effect model of interaction between stock market and economy In this four-phase model, each phase can last for an extended period of time that is not predetermined ex ante. The stock market boom can short lived, or extended. At this stage of our knowledge, all the precise conditions for either event remain a puzzle. However, the discussion so far offers some possible explanations for the conditions for a sustained stock market rally (the ‘wealth effect’ being one of those conditions). Nevertheless, many questions remain, awaiting future study. As the model implies, we may have short recessions and long recoveries or vice versa. Historically, in the post War World II era, recessions got shorter and recoveries longer, perhaps due to conditions consistent with those outlined in the last box in phase 4. Still, a lot of questions remain, with respect to uncertainty in technological innovation and with respect to government policies which may stray from the process described above for different political reasons, causing deviations from the model. Investors price stocks based on the expected earnings and cash from them in the future. The stock market as a whole is a collection of individual corporate stocks, and the market has to be linked in the long-run to the economy’s actual ability to earn those earnings from delivering goods and services. Investors need money to buy stocks and the financial resources, in aggregate, have to be linked not only to future but also to previous economic output that produced them in the first place. Hence, a dynamic and continuous linkage is implied. The essence of the symbiotic relationship hypothesis of this paper is that the value of all the publicly held stocks in America is linked to stream of the existing economic activity, when the later is valued as a perpetual stream which investors discount by a risk adjusted rate. This produces the following perpetuity function: Value of stock market = f (present value of aggregate future economic output),

(1)

where f indicates ‘a function of’. If we proxy the value of publicly traded stocks by the broadest available index, the Wilshire 5000 index, and if we arbitrarily assume to proxy the aggregate economic stream by GDP and the risk adjusted discount rate by the yield on Baa rated bonds, this would produce the following relationship:
 GDP Wilshire 5000 = f . (2) Baa It is very important to emphasize that Eq. (2) represents a long-term relationship and does not preclude short-term miss-pricing stemming from investors psychology, overreaction and emotions, etc. Now, let this relation have a coefficient w plus an unpredictable component Z where Z is assumed to be unknown and E(Z) = 0 if Z is random. For simplicity, let WLSRE represent Wilshire 5000 and the symbol × indicate multiplication. We get:
 GDP WLSRE = w + Z, (3) Baa or, WLSRE × Baa = (w × GDP) + (Z × Baa),

(4)

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or,


GDP = Baa ×

WLSRE w




−Z×

 Baa . w

(5)

Since GDP is defined traditionally as GDP = C + INV + GOV + EX,

(5a)

where C is personal consumption (hence PCE), and INV is business investment, and GOV is government spending, and EX is net exports (exports minus imports), it follows from Eqs. (5) and (5a) that

 WLSRE Z × Baa PCE + INV + GOV + EX = Baa − . (5b) w w The random last product term on the right-hand side (Z ×Baa/w), is likely to converge to zero, if Z and Baa are small and if w is not too small. (We shall see in Section 4 that indeed W is closer to one than to zero) So, this product can be ignored. Hence, as shown in Fig. 1, the stock market can affect all the variables in GDP (increase consumer spending, on domestic and imported consumption goods and services, due to a wealth effect, increased government tax revenues and perhaps (not mandatory, however) increased government spending (as occurred indeed in the U.S.A. in 1999, for example), and increased business investments (model driven)). Thus, from Eq. (5b), it follows that PCE = f (WLSRE 5000),

(5c)

INV = f (WLSRE 5000),

(5d)

EX = f (WLSRE 5000).

(5e)

and,

The nature of Eqs. (5d) and (5e) and of GOV is outside the scope of this paper, for space economy. One could for example expect that wealth effects that increase consumer spending would also manifest themselves in increased imports and increased business expenses, tax revenues, etc., all of which are consistent with the model’s predictions. Thus, Eqs. (4), (5) and (5a) through (5e) support the following proposition: Proposition 1. In the long run, the values of GDP and of equity asset values times a risk adjusted cost of capital should interact in a continuos bio-feedback relationship. This may, under the right conditions, create ‘wealth effects’ and if those conditions don’t exist, end up in market bubbles and reversals. Thus, in terms of this models construction, whenever asset values times Baa (on the left-hand side of Eq. (4)) exceed the value of w × GDP (on the right-hand side of Eq. (4)), the stock market becomes overvalued. Another way to look at it is Eq. (6).
 GDP E (WLSRE) = w . (6) Baa

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Whenever, in Eq. (6), the Wilshire rises (falls) in actual value above (below) the value of the right-hand side, we have an actual W(ov) or W(uv) such that: W (ov) > w, for overvalued stock market (relative to the economy),

(7)

W (uv) < w, for undervalued stock market (relative to the economy).

(8)

and,

W is assumed to be some ‘mean’ or ‘normal’ ratio in the intermediate run (to be determined empirically, but assumed to be, based on DeBondt and Thaler’s (1985, 1989) observations about mean reversion, about 6–12 months. In the long run (2 years or more)), W is assumed in this model to be a ratio that changes with new cultures and technologies. For example, the ratio of GDP to Wilshire 5000 was around 0.7 throughout the 1970s and 1980s, moving up and down towards it. Yet, in the 1990s, this ratio moved decisively above 1.0 and by the beginning of 2000 was around 1.4. How does the market eventually return to its intermediate-term ‘normal’ W? To answer that, suppose that we have an overvalued stock market, namely W (ov) > W , or an undervalued stock market, namely W (uv) < W . It such cases,

 GDPI GDP WLSRE = W (ov) >W , (9) Baa Baa or,




GDP WLSRE = W (uv) Baa





 GDP . Baa

(10)

Holders of financial assets have financial wealth that is not matched by the real present value of output flow on which it has claim. In (9) (overvalued stock market), real output of goods and services is in relative short supply and the excess demand for them should encourage more production and drive up their prices. This would thereafter lead to FED tightening (in attempt to fight off anticipated or actual inflation), by raising interest rates or decrease money supply, and the stock market would then fall in response to this tightening. The increasing output of GDP (or reduction in Baa corporate interest rate, as stronger companies now offer lower default premium) will continue until we return to the equilibrium of Eqs. (3) and (6). Suppose that the stock market is now undervalued, as shown in Eq. (10). Here, the supply of real output is too big relative to the value that equity investors are willing to pay for owning the rights to this economic stream. Hence, real output will have to fall. Given excess capacity, companies will find it more difficult to raise new capital for further expansion and will have to pay higher financing costs (which will drive up the Baa rate which in turn will cause companies to cut down their expansion plans). Economic output will shrink. Later, in response to the economic slowdown, the FED will loosen up monetary policy (increase money supply and/or lower interest rates) and investors will return to the stock market, driving up the value of WLSRE in this model, until we return to the equilibrium of Eqs. (3) and (6). This adjustment process can take quite some time to complete itself, and therefore, we may have temporary situations (of duration that has yet to be determined) where inequalities (7)

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and (9) or (8) and (10) exist. However, these inequalities would eventually be resolved by returning to a general equilibrium. Note that in this model, Wilshire 5000 and Baa and GDP are all concurrent values. The value GDP/Baa can be viewed as a present value of a perpetuity of a stream of GDP, which can be interpreted as some proxy for national wealth. Since GDP is not a constant but rather grows over time, so would grow this proxy for national wealth, and, according to this model, the stock market will grow as well as the economy grows, and vice versa. The model has the following implications: (1) The aggregate value of the publicly listed stocks in the U.S.A. (proxied by Wilshire 5000 index) is related to the value of gross domestic product (hence GDP) discounted by a risk adjusted rate (proxied by the yield on Baa rated bonds (hence Baa)). (2) The process towards equilibrium described above, will in the long run motivate consumers to consume more, and business to invest more, following a prolonged period of stock market price increases. A short-term, non-sustainable deviation, will be corrected by a mean reversion in the stock market. A long-term stock market price increase can either create a “bubble”, if the economy fails to catch up, or, create a “wealth effect” that can feed back to the economy and help it catch up with the stock market. (3) A broad participation of consumers in ownership of stocks AND a long persistent, rise in stock prices, AND an ability of businesses to expand output through improved technology and productivity—are ALL important ingredients in sustaining a “wealth effect” that feeds back from the stock market to the economy. As the multi-phase model presented above shows, the various ingredients have to interact with each other in a well defined manner, for a sustainable wealth effect to exist without a stock market bubble bursting. The stock market may lead the economy, but certain conditions must exist for the economy to follow. (4) Furthermore, this convergence process makes volatility to be not a cause but rather a by-product of market returns. This puts a different perspective on the Braun et al. (1995) paper. (5) If Wilshire 5000 goes up (down) in period t − 1 relative to the underlying GDP in that period, and continues to do so also in period t or more, due to whatever reason that caused investors to be ‘bullish’ (‘bearish’), it will have to correct at some point in period t + j later back to the ‘true intrinsic value’, if the underlying economy failed to catch up. Thus, stocks will rise at a slower and slower rate until eventually coming to an inflection period (where the market is ‘flat’) and then reverse direction. Hence, the model implies a positive serial correlation (trend) and autocollinearity, with a decreasing co-directional coefficient in period t + 1 and still smaller in period t + 2 and so on, until it becomes insignificant from zero at period t + j and thereafter reverses sign or just fades away. This will produce auto-correlation of rank j (j = 1, . . . , n), with alternating signs for the residuals. The length of time j is a matter of empirical test and can vary. (6) Investors adjust their consumption spending not only to their current income but also to their wealth. When they become convinced that recent stock market gains repre-

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sent a permanent addition to their wealth—they allow themselves to spend some of it, thereby further feeding back to sustain the economic growth. These perceptions are dependent both on psychological conditions and on the extent of the wealth gain and its duration. Stock prices simultaneously reflect investors’ expectations about the future economic prospects as well as their existing buying power. This in turn drives from past and perceived future wealth. Thus, we would expect that the level of Wilshire and GDP (and personal consumption PCE) to be correlated with past, present and future values. Since stocks new prices’ information is available continuously every day but new GDPI information is published quarterly, the ex post changes need not be correlated immediately, but would be correlated over enough time for the information to be assimilated by investors and consumers.

4. Empirical tests 4.1. Methodology Monthly data about the value weighed Wilshire 5000, dating from its initiation in December 1970 to December 1999 was obtained from the Wilshire Corporation. The Chicago-based daily CRSP tapes provided information on daily market price for the SP500 index. These were used to calculate daily returns and volatility which was later aggregated into monthly returns and volatility. Only index price changes (excluding dividends) were included. Interest rates on Baa rated corporate bonds, 6 months T-bills (hence INT) and 30-year T-bonds (hence TBOND) and GDP and National Income (NI) data were obtained from the Federal Reserve monthly bulletins. The quarterly GDP and NI data were smoothed monthly (by evenly allocating to each month a one-third of the differential for that quarter). The 30-year period was particularly interesting for its economic variety of recessions, economic booms, steep inflations, and economic “normalcy” and a sub-period of sustained economic and stock market boom, yet was recent enough to make it the period of choice for this study. Hence, all the conclusions from this study should be placed in the perspective of this time period. Over 80 regressions were performed, using Autoregression models as well as Ordinary Least Square (OLS) runs. Since it is easy to do data mining with such a massive data base and multitude of regressions, I shall report here only a small portion of the results. I shall concentrate only on those results most directly related to the theory of wealth effects, namely-testing the relation between PCE (personal consumption) and the stock market value (proxied by Wilshire 5000 index), over time intervals ranging from the concurrent up to delayed lags (up to 36 months). Overall, the results strongly support the predictions outlined in Section 3 earlier. 4.2. Results Since the paper suggests that in the long run there should be a strong correlation in the relationship between the stock market value and GDP, the first test is aimed at validating

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Table 1 The relation between GDP and Wilshire 5000 index, January 1970–October 1999 period t

Probability error

(1) Wilshre 5000 = −1212 + 1.03GDP − 0.4A1 − 0.116A2 − 0.17A3a −1.40 0.16 48.50 0.0001 −6.43 0.0001 −1.756 0.0801 −2.66 0.0083 (2) GDP = 1708 + 0.84Wilshre − 0.29A1 − 0.14A2 − 0.17A3b 4.447 0.33 46.05 0.0001 −4.705 0.0001 −2.301 0.0001 −2.709 0.0221 a b

Reg R 2 = 0.8955, total R 2 = 0.9607, Durbin Watson = 1.7734, DFE = 283. Reg R 2 = 0.8886, total R 2 = 0.939, Durbin Watson = 1.7469, DFE = 283.

this suggestion. Table 1 presents in (1) and (2), the autoregression results of Wilshre 5000 (dependent variable) on GDP (independent variable) and then with the roles reversed. Note that these equations have of rank 12 (NLAG = 12, converge = 12, method = ML), but only the significant ranks (or the first A1 and A2, even non-significant), are shown here for space considerations. As Table 1 demonstrates, there is indeed a strong relationship between the stock market and the economy, as implied by the model. This test by itself is not sufficient to establish the nature of the relationship. Hence, more tests are done. In Table 2, the cumulative change in GDP in each 3-month period (on a rolling, monthly smoothed basis), hence GDPF3, is regressed (in autoreg, in order to account for possible auto-correlation), as the dependent variable. The independent variables in the sequence of regressions are the cumulative change in the Wilshire 5000 index in the last 3 months (WLSRF3), last 6 months (WLSRF6), 12 months (WLSRF12), 18 months (WLSRF18), 24 months (WLSRF24) and last 36 months (WLSRF36). As is seen from Table 2, for the first 3 and 6 months, the coefficients of the independent variables of the stock market are insignificant, indicating that concurrent GDP is not related to the most previous recent stock market of last 3 and 6 months intervals. However, the coefficients and the R2 become significant at the longer time intervals of 12, 18, 24 and 36 months, with R2 increasing consistently (from 0.0 for WLSRF3 all the way up to 0.4985 for WLSRF24), just as the model predicted. The coefficients themselves are in the 0.02–0.03 range, indicating that the delayed GDP wealth effect is about 2.5 cents in GDP for every dollar gained in previous stock market wealth. Moreover, the wealth effect reaches a maximum of 0.03 at 18 months and very slowly declines after that, to 0.024 at 24 months and to 0.0195 after 36 months. These results are consistent with the model’s theory that investors/consumers are at first skeptical of their stock market gains and begin to view them as permanent gains in their wealth, from which they allow themselves to gradually increase spending. This may explain also, the

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Table 2 The relation between change in GDP over the last 3 months (GDPF3) and cumulative changes in Wilshire 5000 index over previous time intervals t

Probability error

(1) GDPF3 = 91.44 + 0.001WLSRF3 − 0.16A1 − 0.04A2a 7.45 0.0001 0.190 0.849 −2.464 0.0144 −0.700 0.484 (2) GDPF3 = 941 + 0.288WLSRDF6 − 0.136A1 + 0.05A2b 0.407 0.68 0.552 0.58 −2.261 0.025 0.839 0.402 (3) GDPF3 = 74.3 + 0.027WLSDF12 − 0.078A1 − 0.03A2c 18.86 0.0001 7.03 0.0001 −1.186 0.237 −0.508 0.611 (4) GDPF3 = 65.42 + 0.03WLSRF18 + 0.005A1 + 0.01A2 + 0.12A3d 24.26 0.0001 12.078 0.0001 0.071 0.94 0.153 0.87 1.794 0.074 (5) GDPF3 = 63.82 + 0.024WLSRF24 + 0.003A1 + 0.06A2 + 0.117A3e 29.71 0.0001 15.42 0.0001 0.045 0.97 0.09 0.92 1.775 0.077 (6) GDPF3 = 62.71 + 0.0195WLSRF36 − 0.018A1 + 0.02A2 + 0.13A3f 25.03 0.0001 14.286 0.0001 −0.26 0.79 0.31 0.75 1.921 0.056 Reg R 2 = 0.000, total R 2 = 0.1954, Durbin Watson = 1.90, DFE = 269. Reg R 2 = 0.0011, total R 2 = 0.2202, Durbin Watson = 1.9936, DFE = 277. c Reg R 2 = 0.1732, total R 2 = 0.2763, Durbin Watson = 1.8905, DFE = 260. d Reg R 2 = 0.3765, total R 2 = 0.3922, Durbin Watson = 1.8408, DFE = 254. e Reg R 2 = 0.4985, total R 2 = 0.4426, Durbin Watson = 1.8897, DFE = 248. f Reg R 2 = 0.4701, total R 2 = 0.4421, Durbin Watson = 1.8978, DFE = 237. a

b

drop in personal savings rate in the U.S.A. in the decade of the 1990s, corresponding to the bull market in stock during that period. Tables 1 and 2 present the relationship between the stock market and the economy. Next, we examine more closely the direct wealth effect interaction between the concurrent consumer spending (in the last 3 and 6 months, respectively) and previous stock market gains, at previous time intervals, to see if the results are consistent with the model’s predictions.

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Table 3 What affects the last 3 months change in personal private consumption t

Probability error

(1) PCEG3 = 58.5 − 0.003887WLSRDF3 − 1.191A1 − 0.0828A2 − 0.171A3a 12.942 0.0001 −0.783 0.4341 −3.172 0.0017 −1.338 0.1820 −2.770 0.0060 (2) PCEG3 = 54.81 + 0.01634WLS3F6 − 0.152A1 − 0.0379A2 − 0.146A3b 21.117 0.0001 −0.783 0.0011 −3.291 0.0165 −2.143 0.5651 −0.576 0.0250 (3) PCEG3 = 50.496 + 0.01677WLS3F12 − 0.175A1 − 0.0386A2 − 0.143A3c 21.853 0.0001 −0.783 0.0001 4.829 0.0069 −2.724 0.5729 −0.565 0.0361 (4) PCEG3 = 46.166 + 0.01618WLS3F18 − 0.0734A1 − 0.056A2 − 0.093A3d 27.942 0.0001 8.625 0.0001 −1.132 0.2589 −0.813 0.4168 −1.376 0.1700 (5) PCEG3 = 45.2167 + 0.01307WLS3F24 − 0.0365A1 − 0.011A2 − 0.088A3e 28.071 0.0001 9.394 0.0001 −1.132 0.5792 −0.555 0.8673 −1.167 0.1960 (6) PCEG3 = 46.091 + 0.009227WLS3F36 − 0.0434A1 − 0.0055A2 − 0.086A3f 28.045 0.0001 8.687 0.0001 −0.648 0.5179 −0.079 0.9374 −1.241 0.2159 Reg R 2 = 0.0042, total R 2 = 0.1406, Durbin Watson = 2.0159, DFE = 277. Reg R 2 = 0.0411, total R 2 = 0.1253, Durbin Watson = 1.9880, DFE = 263. c Reg R 2 = 0.0880, total R 2 = 0.1663, Durbin Watson = 1.99979, DFE = 257. d Reg R 2 = 0.2360, total R 2 = 0.2507, Durbin Watson = 1.9986, DFE = 251. e Reg R 2 = 0.2360, total R 2 = 0.2507, Durbin Watson = 1.9986, DFE = 251. f Reg R 2 = 0.2529, total R 2 = 0.2491, Durbin Watson = 2.011, DFE = 234. a

b

Tables 3 and 4 show the results. In these tables, PCEG3 represents the cumulative change in personal consumption in the last 3 months (and PCEG6 represents it for the last 6 months). WLSDF6 represents the change in the Wilshire 5000 index during the last 6 months. WLS6F12 represents the change in the Wilshire 5000 index from month t-12 to month t-6. WLS6F18 represents the change in the stock index from month t-18 to t-6, and so on. This procedure was

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Table 4 Are the changes in consumption in the last 6 month related to changes in the stock market in the previous months t

Probability error

(1) PCEG6 = 116.96 − 0.000924WLSDF6 − 0.183A1 − 0.123A2 − 0.113A3a 9.685 0.0001 −0.157 0.8753 −2.989 0.0031 −1.977 0.0490 −1.815 0.0706 (2) PCEG6 = 111.37 + 0.008885WLS6F12 − 0.1395A1 − 0.085A2 − 0.156A3b 9.0090 0.0001 0.00609 0.1458 0.0642 0.0308 0.0679 0.2139 0.0673 0.0207 (3) PCEG6 = 99.35 + 0.027023WLS6F18 − 0.06486A1 − 0.0938A2 − 0.10A3c 24.33 0.0001 6.844 0.001 −0.991 0.3226 −1.342 0.1809 −1.522 0.1293 (4) PCEG6 = 91.98 + 0.027241WLS6F24 + 0.031612A1 − 0.0245A2d 35.916 0.0001 11.243 0.0001 0.476 0.6343 −0.361 0.7184 (5) PCEG6 = 92.89 + 0.0194WLSR6F36 + 0.028A1 + 0.036A2 − 0.004A3e 41.236 0.0001 11.906 0.0001 0.411 0.6812 0.510 0.6104 −0.068 0.9461 Reg R 2 = 0.0020, total R 2 = 0.654, Durbin Watson = 2.0082, DFE = 274. Reg R 2 = 0.0071, total R 2 = 0.2362, Durbin Watson = 1.9072, DFE = 257. c Reg R 2 = 0.1556, total R 2 = 0.2967, Durbin Watson = 1.9277, DFE = 251. d Reg R 2 = 0.3424, total R 2 = 0.3901, Durbin Watson = 1.9222, DFE = 245. e Reg R 2 = 0.3819, total R 2 = 0.3703, Durbin Watson = 1.897, DFE = 234. a

b

chosen in order to validate the model’s implication that the consumption in the last 6 months is related to extended stock market gains in time intervals prior to that. As can be seen from the tables, the results are indeed consistent with the model’s predictions. As Tables 3 and 4 demonstrate, consumers do indeed pick up (reduce) their spending when they see that the stock market gains (losses) were sustainable. The longer the time period back that investors had observed their stock market gains (losses) go on, the more inclined they were to view them as a permanent gain (loss) to their wealth, as seen in Table 3, when the dependent variable is PCEF3 (the change in personal consumption during the concurrent last 3 months), the slopes of the changes in the stock market value over the concurrent last 3 months (WLSRDF3) and the previous 3 months (WLSR3F6) and the previous 9 months

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Fig. 1. The delayed stock market wealth effect on consumption in the last 3 months.

(recall, WLSR3F12, is the change in Wilshire 5000 index from period t-12 months through t-3 months) are not significantly different from zero, The slopes become significant after 15 months (WLS3F18) with a 0.016 coefficient. The slope stays significant at 0.013 coefficient after 21 months (WLS3F24) and decays gradually to a coefficient of 0.009 after 33 months (WLS3F36). These are long time intervals, indicating an extended persistence of the wealth effect for longer than one would expect, after a slow start. Perhaps the failure of earlier studies to detect and measure the wealth effect stems from this slow start in effect build-up. The effect is bigger when we test the change in personal consumption in the concurrent last 6-month period (PCEG6). As seen in Table 4, the coefficients are not significantly different from zero for the first 6 and 9 months (WLSDF6 and WLS6F12, respectively) and grow to a statistically significant 0.027 after 12 months (WLS6F18) and remain at 0.027 also up to 18 months (WLS6F24), after which it begins to slowly decay. Nevertheless, it remains statistically significant even up to 30 months (WLS5F36), at 0.019. Figs. 1 and 2 illustrate these relationships graphically. These tests looked at the impact that the stock market had on the economy, mostly through its wealth effect on consumer spending. While the interaction of the stock market and business investments are the subject of another, future study, it is useful here to briefly look into possible linkage there. As Table 5 shows, here as well we see that business investment reacts sooner, being non-reactive at the first 6 months (with a small positive immediate response in the first 3 months), and rising (falling) after 6 more months of a strong (weak) as seen from the coefficient of WLS6F12, where sufficient evidence is accumulated that the gains (loses) in the stock market are sustained long enough. The faster response of business investments to a strong stock market relative to consumer response may simply indicate that businesses are

Fig. 2. The delayed stock market wealth effect on consumption in the last 6 months.

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Table 5 The relation between gross domestic investments in the concurrent 3- and/or 6-month period (GDPIF3 and GDPIF6) and changes in the Wilshire 5000 index in previous months t

Probability error

(1) GDPIF3 = 2405 + 0.35WLSDF3 − 0.196A1 − 0.17A2 − 0.27A3a 3.361 0.0009 2.596 0.010 −3.085 0.022 −2.638 0.0088 −4.154 0.0001 (2) GDPIF6 = 30.4 + 0.0023WLSDF6 − 0.17A1 + 0.07A2 − 0.12A3b 4.26 0.0001 0.353 0.724 −2.847 0.0041 1.250 0.219 −1.990 0.0476 (3) GDPI6F12 = 24.5 + 0.0252WLS6F12 − 0.15A1 + 0.09A2 − 0.07A3c 4.861 0.0001 4.085 0.0001 −2.458 0.0146 1.472 0.1422 −1.101 0.2722 (4) GDPIF6 = 20.83 + 0.023WLS6F18 − 0.116A1 + 0.08A2 − 0.06A3d 4.586 0.0001 4.896 0.0001 −1.778 0.0765 1.302 0.194 −0.892 0.327 (5) GDPIF6 = 20.08 + 0.015WLS6F24 − 0.15A1 + 0.08A2 − 0.06A3e 4.278 0.0001 4.017 0.0001 −2.376 0.0183 1.244 0.2148 −1.056 0.2921 (6) GDPIF6 = 18.39 + 0.011WLS6F36 − 0.17A1 + 0.099A2 − 0.06A3f 3.55 0.005 3.808 0.0002 −2.508 0.0128 1.469 0.1432 −0.958 0.3399 Interpretation and implications for the magnitude of the “wealth effect”. a Reg R 2 = 0.0294, total R 2 = 0.5388, Durbin Watson = 1.91323, DFE = 269. b Reg R 2 = 0.0003, total R 2 = 0.1254, Durbin Watson = 1.9866, DFE = 277. c Reg R 2 = 0.063, total R 2 = 0.182, Durbin Watson = 2.076, DFE = 260. d Reg R 2 = 0.0879, total R 2 = 0.1963, Durbin Watson = 2.0924, DFE = 254. e Reg R 2 = 0.0654, total R 2 = 0.1812, Durbin Watson = 2.112, DFE = 248. f Reg R 2 = 0.0605, total R 2 = 0.1771, Durbin Watson = 21.1021, DFE = 237.

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more opportunistic than consumes, trying to capitalize on a strong stock market in raising capital and financing new capital outlays. Note that the R2 is smaller in Table 5 than in Table 3 or 4, indicating that the previously discussed wealth effect interaction of the stock market with the economy is more due to its impact on consumer spending than on other factors. The Wilshire 5000 index increased from 7.198 trillion dollars in December 1996 to 9.298 trillion dollars by December 1997, a gain of $2100 billion. The PCEG6 coefficient for WLS6F18 is (see Table 4) 0.027. Thus, the wealth effect of the 12-month stock wealth gain in 1998 on the next first 6 months of 1998 of personal consumption was 0.027 times $2100 or 56.7 billion dollars. During the same 6-month period in 1998 personal consumption increases 137 billion dollars, from 5636 billion to 5773 billion dollars. Hence, the stock market wealth gain in the 12 months of 1997 was responsible for 41% (56.7/137) of the increase in personal consumption in the subsequent 6 months of 1998. Likewise, the Wilshire 5000 index increased to 13,230 billion dollars by December of 1999. That is an increase of 6,620 billion dollars from December 1997. From Table 2, we see that the coefficient of GDPF3 for WLSRF36 was 0.0195. This implies that 129 billion dollars (0.0195 times 6620) of GDP in the first 3 months of the year 2000 are due to the wealth effect of the stock market gain in the preceding 36 months. While at the time of writing this paper, the GDP for the first quarter of 2000 is not available yet, it is likely to be a significant share of the GDP growth in that period. It is important to note that by the fourth quarter of 1999, the GDP was growing at a very high 6.9%. This strong growth rate is consistent with this paper’s story of the carry-over of wealth effects from the stock market to the economy after consumers (and probably businesses as well, although that is a subject for a separate study), had enough time to convince themselves that the stock market gains represent a permanent addition to their wealth. Those wealth effects were making it harder for the Federal Reserve to slow down the economy through monetary means of raising interest rates and slowing down the growth of the monetary base. The model and the results imply also, indirectly, that the Federal Reserve now should include in its monetary policy considerations an additional element, the wealth effect interaction between the stock market and the economy.

5. Summary and conclusion Stock investors are skeptical at first about the sustainability of their wealth gains and start spending some of it at a slowly increasing rate which peaks at about 2.6% of the cumulative gains over 18–24 months, although some effect lingers on up to 36 months. The 6 trillion dollar stock market gain over the period 1997 through 1999, e.g., is responsible for over 120 billion dollars of consumption spending in 1998 and also in 1999 and is projected to contribute significantly into 2000, thus, partly offsetting the negative effect on the economy from a series of interest rate increases by the Federal Reserve. The Chairman of the Federal Reserve expressed in October 1999 and early 2000 his concerns that the relationship of the stock market and the economy and any implied wealth effect between them remain a puzzle. It was obvious from his other remarks in the previous years that the FED was concerned about the “irrational exuberance” by investors. However, the FED was also concerned that aggressive attempts to deflate the stock market (while fighting possible inflation due to a strong economy), might boomerang into a severe economic slow-down. Such concerns could be traced to perceived

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relationships between the stock market crash of 1929 and the subsequent depression in the U.S.A. and to the severe economic recession of the 1990s in Japan following its central bank’s raising interest rates in 1989 in response to a very strong stock market. The findings of this paper offer a recent perspective on this issue, and lend support to such concerns. This paper explores recent evidence in the U.S.A. about the relationship between the stock market and the economy and finds significant wealth effects that manifest themselves in a particular manner that eluded previous studies. Thus, while the wealth effect is very weak in the short run, it is quite significant in the long run. It was shown here that a significant share of the change in consumer spending in 1998 and 1999 was due to stock market gains in previous years. The paper offered two possible explanations for this interaction: one explanation is based on a new paradigm that perceives the economy and the stock market as bio-systems that interact with each other in a symbiotic relationship in which they feed each other. The second explanation is more in line with traditional financial economics. The process of creating wealth effects is shown to provide, as a by-product, some explanations for several other issues that have puzzled scholars and policy makers for a long time. However, many new questions arise. The new paradigm does not displace traditional economics, and the results can stand firmly on the basis of traditional methods of financial economic analysis, reinforced with some widely accepted notions about investors’ psychology. Nevertheless, the results are also consistent with predictions that are directly following from the new paradigm, thereby supporting them as well. Future studies are needed in order to examine more the new paradigm and its implications. What if the bio-system paradigm interpretation is correct? Acknowledgments I would like to thank participants in meetings of the Financial Management Association in Chicago (1998), the European Finance Management Association (1999 and 1998) for comments on earlier versions of the paper. Thanks also to Speros Margetis and Sean Murphy for research assistance. The usual disclaimer applies. I alone bear responsibility for the ideas, findings and errors. References Braun, P.A., Nelson, D.B., Sunier, A.M., 1995. Good news, bad news, volatility and betas. Journal of Finance, 1575–1603. Bulmash, S.B., Trivoli, G.W., 1991. Time lagged interactions between stock prices and selected economic variables. Journal of Portfolio Management 17 (Summer), 61–67. Coase, R.H., 2000. Talking about tomorrow, Ronald Coase, interview and feature article. The Wall Street Journal, R36. Cote, J., Goodstein, J., 2001. A Breed Apart? Security Analysts and Herding Behavior. Kluwer Academic Publishers, New York. Journal of Business Ethics 18(3) 305–314. Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market under and overreaction. Journal of Finance, 1839–1885. DeBondt, W.F.M., Forbes, W.P., 1998. Herding in analysts earnings forecasts: evidence from the United Kingdom. Working Paper, University of Wisconsin-Madison.

100

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DeBondt, F.M., Thaler, R.H., 1985. Does the stock market overreact? Journal of Finance 40, 793–805. DeBondt, F.M., Thaler, R.H., 1989. Anomalies: a mean-reverting walk down Wall Street. Journal of Economic Perspectives 3, 189–202. Fama, E.F., 1990. Stock returns, expected returns and real activity. Journal of Finance, 1089–1107. Fama, E.F., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 50, 131–155. Fama, E.F., French, K.R., 1996. Multi-factor explanations of assets pricing anomalies. Journal of Finance, 55–84. Farmer, R.E., Guo, J.-T., 1994. Real business cycles and the animal spirits hypothesis. Journal of Economic Theory 63 (1), 42–73. Friedman, M., 1957. Theory of the Consumption Function. Princeton University Press, Princeton, NJ. Gordon, M.J., 1963. Optimal investment and financing policy. Journal of Finance, 264–272. Haugen, R.A., Baker, N.L., 1996. Commonality in the determinants of expected stock returns. Journal of Financial Economics, 401–440. Higgins, B., 1988. Is a recession inevitable this year? Economic review. Federal Reserve Bank of Kansas City, pp. 3–16. Howitt, P., McAfee, P.R., 1992. Animal spirits. American Economic Review 82 (3), 493–508. Ip, G., 2000. How a drop in stocks could slam the economy. The Wall Street Journal, C1. Jagadeesh, N., Titman, S., 1993. Returns to buying winners and selling losers: implications for stock market efficiency. Journal of Finance 48, 65–91. Jagannathan, R., Wang, Z., 1996. The conditional CAPM and the cross-section of expected returns. Journal of Finance, 3–54. Jensen, G.R., Mercer, J.M., Johnson, R.R., 1996. Business conditions, monetary policy, and expected security returns. Journal of Financial Economics, 185–212. Kahneman, D., Riepe, M., 1998. Aspects of investor psychology. Journal of Portfolio Management 24 (4), 52–65. Keynes, J.M., 1936. The general theory of employment, interest, and money. Harcourt, Brace and Company, New York. Kidwell, D.S., Peterson, R., Blackwell, D.W., 2000. Financial Institutions, Markets and Money, 7th Edition. Dryden Press, Harcourt College Publishers, pp. 288–290 (Chapter 9). Kupic, P., Sharp, S.A., 1991. Animal spirits, margin requirements, and stock price volatility. Journal of Finance 46 (2), 717–792. McQueen, G., Pinegar, M., Thorley, S., 1996. Delayed reaction to good news and cross auto-correlation of portfolio returns. Journal of Finance, 889–919. Middleton, E., 1996. Adaptation level and animal spirits. Journal of Economic Psychology 17 (4), 479–499. Remolona, E.M., 1991. Global stock markets and links in real activity. Research Paper no. 9109, Federal Reserve Bank of New York. Tversky, A., Kahneman, D., 1986. Rational choices and the framing of decisions. Journal of Business 59 (4), 251–278. Weil, P., 1989. Increasing returns and animal spirits. American Economic Review 79 (4), 889–995.